OpenRaiser/Intern-Atlas huggingface dataset:
Strongly Monotone Drawings of Planar Graphs
Venue: arXiv cs.CG 2016
Year: 2016

A straight-line drawing of a graph is a monotone drawing if for each pair of vertices there is a path which is monotonically increasing in some direction, and it is called a strongly monotone drawing if the direction of monotonicity is given by the direction of the line segment connecting the two vertices. We present algorithms to compute crossing-free strongly monotone drawings for some classes of planar graphs; namely, 3-connected planar graphs, outerplanar graphs, and 2-trees. The drawings of 3-connected planar graphs are based on primal-dual circle packings. Our drawings of outerplanar graphs are based on a new algorithm that constructs strongly monotone drawings of trees which are also convex. For irreducible trees, these drawings are strictly convex.
NodIO, a JavaScript framework for volunteer-based evolutionary algorithms : first results
Venue: arXiv cs.DC 2016
Year: 2016

JavaScript is an interpreted language mainly known for its inclusion in web browsers, making them a container for rich Internet based applications. This has inspired its use, for a long time, as a tool for evolutionary algorithms, mainly so in browser-based volunteer computing environments. Several libraries have also been published so far and are in use. However, the last years have seen a resurgence of interest in the language, becoming one of the most popular and thus spawning the improvement of its implementations, which are now the foundation of many new client-server applications. We present such an application for running distributed volunteer-based evolutionary algorithm experiments, and we make a series of measurements to establish the speed of JavaScript in evolutionary algorithms that can serve as a baseline for comparison with other distributed computing experiments. These experiments use different integer and floating point problems, and prove that the speed of JavaScript is actually competitive with other languages commonly used by the evolutionary algorithm practitioner.
Automatic Construction of Evaluation Sets and Evaluation of Document Similarity Models in Large Scholarly Retrieval Systems
Venue: arXiv cs.IR 2016
Year: 2016

Retrieval systems for scholarly literature offer the ability for the scientific community to search, explore and download scholarly articles across various scientific disciplines. Mostly used by the experts in the particular field, these systems contain user community logs including information on user specific downloaded articles. In this paper we present a novel approach for automatically evaluating document similarity models in large collections of scholarly publications. Unlike typical evaluation settings that use test collections consisting of query documents and human annotated relevance judgments, we use download logs to automatically generate pseudo-relevant set of similar document pairs. More specifically we show that consecutively downloaded document pairs, extracted from a scholarly information retrieval (IR) system, could be utilized as a test collection for evaluating document similarity models. Another novel aspect of our approach lies in the method that we employ for evaluating the performance of the model by comparing the distribution of consecutively downloaded document pairs and random document pairs in log space. Across two families of similarity models, that represent documents in the term vector and topic spaces, we show that our evaluation approach achieves very high correlation with traditional performance metrics such as Mean Average Precision (MAP), while being more efficient to compute.
Anti-commutative Dual Complex Numbers and 2D Rigid Transformation
Venue: arXiv cs.GR 2016
Year: 2016

We introduce a new presentation of the two dimensional rigid transformation which is more concise and efficient than the standard matrix presentation. By modifying the ordinary dual number construction for the complex numbers, we define the ring of the anti-commutative dual complex numbers, which parametrizes two dimensional rotation and translation all together. With this presentation, one can easily interpolate or blend two or more rigid transformations at a low computational cost. We developed a library for C++ with the MIT-licensed source code and demonstrate its facility by an interactive deformation tool developed for iPad.
A toolbox to solve coupled systems of differential and difference equations
Venue: arXiv cs.SC 2016
Year: 2016

We present algorithms to solve coupled systems of linear differential equations, arising in the calculation of massive Feynman diagrams with local operator insertions at 3-loop order, which do {\it not} request special choices of bases. Here we assume that the desired solution has a power series representation and we seek for the coefficients in closed form. In particular, if the coefficients depend on a small parameter $\ep$ (the dimensional parameter), we assume that the coefficients themselves can be expanded in formal Laurent series w.r.t.\ $\ep$ and we try to compute the first terms in closed form. More precisely, we have a decision algorithm which solves the following problem: if the terms can be represented by an indefinite nested hypergeometric sum expression (covering as special cases the harmonic sums, cyclotomic sums, generalized harmonic sums or nested binomial sums), then we can calculate them. If the algorithm fails, we obtain a proof that the terms cannot be represented by the class of indefinite nested hypergeometric sum expressions. Internally, this problem is reduced by holonomic closure properties to solving a coupled system of linear difference equations. The underlying method in this setting relies on decoupling algorithms, difference ring algorithms and recurrence solving. We demonstrate by a concrete example how this algorithm can be applied with the new Mathematica package \texttt{SolveCoupledSystem} which is based on the packages \texttt{Sigma}, \texttt{HarmonicSums} and \texttt{OreSys}. In all applications the representation in $x$-space is obtained as an iterated integral representation over general alphabets, generalizing Poincaré iterated integrals.
A Scalable Low-Cost-UAV Traffic Network (uNet)
Venue: arXiv cs.SY 2016
Year: 2016

This article proposes a new Unmanned Aerial Vehicle (UAV) operation paradigm to enable a large number of relatively low-cost UAVs to fly beyond-line-of-sight without costly sensing and communication systems or substantial human intervention in individual UAV control. Under current free-flight-like paradigm, wherein a UAV can travel along any route as long as it avoids restricted airspace and altitudes. However, this requires expensive on-board sensing and communication as well as substantial human effort in order to ensure avoidance of obstacles and collisions. The increased cost serves as an impediment to the emergence and development of broader UAV applications. The main contribution of this work is to propose the use of pre-established route network for UAV traffic management, which allows: (i) pre- mapping of obstacles along the route network to reduce the onboard sensing requirements and the associated costs for avoiding such obstacles; and (ii) use of well-developed routing algorithms to select UAV schedules that avoid conflicts. Available GPS-based navigation can be used to fly the UAV along the selected route and time schedule with relatively low added cost, which therefore, reduces the barrier to entry into new UAV-applications market. Finally, this article proposes a new decoupling scheme for conflict-free transitions between edges of the route network at each node of the route network to reduce potential conflicts between UAVs and ensuing delays. A simulation example is used to illustrate the proposed uNet approach.
Predicting the large-scale evolution of tag systems
Venue: arXiv cs.FL 2016
Year: 2016

We present a method for predicting the large-scale evolution of a tag system from its production rules. A tag system's evolution is first divided into stages called `epochs' in which the tag system evolves monotonously. The distribution of symbols in the queue at the beginning of each epoch determines the tag system's large-scale properties, including growth rate and string densities, during that epoch. We derive the symbol distribution for the next epoch from the distribution for the current one, using this to make predictions over multiple successive epochs. Finally, we compare predictions that were obtained with this method to computer simulations and find that it retains great accuracy over several epochs.
Group Invariant Deep Representations for Image Instance Retrieval
Venue: arXiv cs.CV 2016
Year: 2016

Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors for image instance retrieval. While CNN-based descriptors are generally remarked for good retrieval performance at lower bitrates, they nevertheless present a number of drawbacks including the lack of robustness to common object transformations such as rotations compared with their interest point based FV counterparts. In this paper, we propose a method for computing invariant global descriptors from CNNs. Our method implements a recently proposed mathematical theory for invariance in a sensory cortex modeled as a feedforward neural network. The resulting global descriptors can be made invariant to multiple arbitrary transformation groups while retaining good discriminativeness. Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated. We also show that our method which has few parameters is not prone to overfitting: improvements generalize well across datasets with different properties with regard to invariances. Finally, we show that our descriptors are able to compare favourably to other state-of-the-art compact descriptors in similar bitranges, exceeding the highest retrieval results reported in the literature on some datasets. A dedicated dimensionality reduction step --quantization or hashing-- may be able to further improve the competitiveness of the descriptors.
Exploiting Context-Awareness for Secure Spectrum Trading in Multi-hop Cognitive Cellular Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we consider context-awareness to enhance route reliability and robustness in multi-hop cognitive networks. A novel context-aware route discovery protocol is presented to enable secondary users to select the route according to their QoS requirements. The protocol facilitates adjacent relay selection under different criteria, such as shortest available path, route reliability and relay reputation. New routing and security-based metrics are defined to measure route robustness in spatial, frequency and temporal domains. Secure throughput, defined as the percentage of traffic not being intercepted in the network, is provided. The resources needed for trading are then obtained by jointly optimizing secure throughput and trading price. Simulation results show that when there is a traffic imbalance of factor 4 between the primary and secondary networks, 4 channels are needed to achieve 90% link reliability and 99% secure throughput in the secondary network. Besides, when relay reputation varies from 0.5 to 0.9, a 20% variation in the required resources is observed.
Kernelized LRR on Grassmann Manifolds for Subspace Clustering
Venue: arXiv cs.CV 2016
Year: 2016

Low rank representation (LRR) has recently attracted great interest due to its pleasing efficacy in exploring low-dimensional sub- space structures embedded in data. One of its successful applications is subspace clustering, by which data are clustered according to the subspaces they belong to. In this paper, at a higher level, we intend to cluster subspaces into classes of subspaces. This is naturally described as a clustering problem on Grassmann manifold. The novelty of this paper is to generalize LRR on Euclidean space onto an LRR model on Grassmann manifold in a uniform kernelized LRR framework. The new method has many applications in data analysis in computer vision tasks. The proposed models have been evaluated on a number of practical data analysis applications. The experimental results show that the proposed models outperform a number of state-of-the-art subspace clustering methods.
A Class of Three-Weight Linear Codes and Their Complete Weight Enumerators
Venue: arXiv cs.IT 2016
Year: 2016

Recently, linear codes constructed from defining sets have been investigated extensively and they have many applications. In this paper, for an odd prime $p$, we propose a class of $p$-ary linear codes by choosing a proper defining set. Their weight enumerators and complete weight enumerators are presented explicitly. The results show that they are linear codes with three weights and suitable for the constructions of authentication codes and secret sharing schemes.
Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
Venue: arXiv cs.LG 2016
Year: 2016

Predicting user responses, such as click-through rate and conversion rate, are critical in many web applications including web search, personalised recommendation, and online advertising. Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known. Major user response prediction models have to either limit themselves to linear models or require manually building up high-order combination features. The former loses the ability of exploring feature interactions, while the latter results in a heavy computation in the large feature space. To tackle the issue, we propose two novel models using deep neural networks (DNNs) to automatically learn effective patterns from categorical feature interactions and make predictions of users' ad clicks. To get our DNNs efficiently work, we propose to leverage three feature transformation methods, i.e., factorisation machines (FMs), restricted Boltzmann machines (RBMs) and denoising auto-encoders (DAEs). This paper presents the structure of our models and their efficient training algorithms. The large-scale experiments with real-world data demonstrate that our methods work better than major state-of-the-art models.
Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation
Venue: arXiv cs.LG 2016
Year: 2016

User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users' interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user's interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users' ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines.
Directional Beamforming for Millimeter-Wave MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

The focus of this paper is on beamforming in a millimeter-wave (mmW) multi-input multi-output (MIMO) setup that has gained increasing traction in meeting the high data-rate requirements of next-generation wireless systems. For a given MIMO channel matrix, the optimality of beamforming with the dominant right-singular vector (RSV) at the transmit end and with the matched filter to the RSV at the receive end has been well-understood. When the channel matrix can be accurately captured by a physical (geometric) scattering model across multiple clusters/paths as is the case in mmW MIMO systems, we provide a physical interpretation for this optimal structure: beam steering across the different paths with appropriate power allocation and phase compensation. While such an explicit physical interpretation has not been provided hitherto, practical implementation of such a structure in a mmW system is fraught with considerable difficulties (complexity as well as cost) as it requires the use of per-antenna gain and phase control. This paper characterizes the loss in received SNR with an alternate low-complexity beamforming solution that needs only per-antenna phase control and corresponds to steering the beam to the dominant path at the transmit and receive ends. While the loss in received SNR can be arbitrarily large (theoretically), this loss is minimal in a large fraction of the channel realizations reinforcing the utility of directional beamforming as a good candidate solution for mmW MIMO systems.
Subexponential time algorithms for finding small tree and path decompositions
Venue: arXiv cs.DS 2016
Year: 2016

The Minimum Size Tree Decomposition (MSTD) and Minimum Size Path Decomposition (MSPD) problems ask for a given n-vertex graph G and integer k, what is the minimum number of bags of a tree decomposition (respectively, path decomposition) of G of width at most k. The problems are known to be NP-complete for each fixed $k\geq 4$. We present algorithms that solve both problems for fixed k in $2^{O(n/ \log n)}$ time and show that they cannot be solved in $2^{o(n / \log n)}$ time, assuming the Exponential Time Hypothesis.
Good Self-dual Generalized Quasi-Cyclic Codes Exist
Venue: arXiv cs.IT 2016
Year: 2016

We show that there are good long binary generalized quasi-cyclic self-dual (either Type I or Type II) codes.
Joint Optimization of Cloud and Edge Processing for Fog Radio Access Networks
Venue: arXiv cs.IT 2016
Year: 2016

This work studies the joint design of cloud and edge processing for the downlink of a fog radio access network (F-RAN). In an F-RAN, as in cloud-RAN (C-RAN), a baseband processing unit (BBU) can perform joint baseband processing on behalf of the remote radio heads (RRHs) that are connected to the BBU by means of the fronthaul links. In addition to the minimal functionalities of conventional RRHs in C-RAN, the RRHs in an F-RAN may be equipped with local caches, in which frequently requested contents can be stored, as well as with baseband processing capabilities. They are hence referred to as enhanced RRH (eRRH). This work focuses on the design of the delivery phase for an arbitrary pre-fetching strategy used to populate the caches of the eRRHs. Two fronthauling modes are considered, namely a hard-transfer mode, whereby non-cached files are communicated over the fronthaul links to a subset of eRRHs, and a soft-transfer mode, whereby the fronthaul links are used to convey quantized baseband signals as in a C-RAN. Unlike the hard-transfer mode in which baseband processing is traditionally carried out only at the eRRHs, the soft-transfer mode enables both centralized precoding at the BBU and local precoding at the eRRHs based on the cached contents, by means of a novel superposition coding approach. To attain the advantages of both approaches, a hybrid design of soft- and hard-transfer modes is also proposed. The problem of maximizing the delivery rate is tackled under fronthaul capacity and per-eRRH power constraints. Numerical results are provided to compare the performance of hard- and soft-transfer fronthauling modes, as well as of the hybrid scheme, for different baseline pre-fetching strategies.
Trans-gram, Fast Cross-lingual Word-embeddings
Venue: arXiv cs.CL 2016
Year: 2016

We introduce Trans-gram, a simple and computationally-efficient method to simultaneously learn and align wordembeddings for a variety of languages, using only monolingual data and a smaller set of sentence-aligned data. We use our new method to compute aligned wordembeddings for twenty-one languages using English as a pivot language. We show that some linguistic features are aligned across languages for which we do not have aligned data, even though those properties do not exist in the pivot language. We also achieve state of the art results on standard cross-lingual text classification and word translation tasks.
A Mobile Phone based Speech Therapist
Venue: arXiv cs.CY 2016
Year: 2016

Patients with articulatory disorders often have difficulty in speaking. These patients need several speech therapy sessions to enable them speak normally. These therapy sessions are conducted by a specialized speech therapist. The goal of speech therapy is to develop good speech habits as well as to teach how to articulate sounds the right way. Speech therapy is critical for continuous improvement to regain normal speech. Speech therapy sessions require a patient to travel to a hospital or a speech therapy center for extended periods of time regularly; this makes the process of speech therapy not only time consuming but also very expensive. Additionally, there is a severe shortage of trained speech therapists around the globe in general and in developing countries in particular. In this paper, we propose a low cost mobile speech therapist, a system that enables speech therapy using a mobile phone which eliminates the need of the patient to frequently travel to a speech therapist in a far away hospital. The proposed system, which is being built, enables both synchronous and asynchronous interaction between the speech therapist and the patient anytime anywhere
Inference rules for RDF(S) and OWL in N3Logic
Venue: arXiv cs.DB 2016
Year: 2016

This paper presents inference rules for Resource Description Framework (RDF), RDF Schema (RDFS) and Web Ontology Language (OWL). Our formalization is based on Notation 3 Logic, which extended RDF by logical symbols and created Semantic Web logic for deductive RDF graph stores. We also propose OWL-P that is a lightweight formalism of OWL and supports soft inferences by omitting complex language constructs.
Human Attention Estimation for Natural Images: An Automatic Gaze Refinement Approach
Venue: arXiv cs.CV 2016
Year: 2016

Photo collections and its applications today attempt to reflect user interactions in various forms. Moreover, photo collections aim to capture the users' intention with minimum effort through applications capturing user intentions. Human interest regions in an image carry powerful information about the user's behavior and can be used in many photo applications. Research on human visual attention has been conducted in the form of gaze tracking and computational saliency models in the computer vision community, and has shown considerable progress. This paper presents an integration between implicit gaze estimation and computational saliency model to effectively estimate human attention regions in images on the fly. Furthermore, our method estimates human attention via implicit calibration and incremental model updating without any active participation from the user. We also present extensive analysis and possible applications for personal photo collections.